Overview

Dataset statistics

Number of variables14
Number of observations50000
Missing cells45647
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory78.0 B

Variable types

NUM12
BOOL1
CAT1

Warnings

engine_capacity has 30050 (60.1%) missing values Missing
damage has 8266 (16.5%) missing values Missing
insurance_price has 7331 (14.7%) missing values Missing
power is highly skewed (γ1 = 52.35179232) Skewed
Ind has unique values Unique
type has 4329 (8.7%) zeros Zeros
power has 4294 (8.6%) zeros Zeros

Reproduction

Analysis started2020-11-03 12:47:49.335185
Analysis finished2020-11-03 12:48:42.067431
Duration52.73 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Ind
Real number (ℝ≥0)

UNIQUE

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50259.7297
Minimum0
Maximum99999
Zeros1
Zeros (%)< 0.1%
Memory size390.8 KiB
2020-11-03T14:48:42.333163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5121.85
Q125387.75
median50349.5
Q375231.75
95-th percentile95043.05
Maximum99999
Range99999
Interquartile range (IQR)49844

Descriptive statistics

Standard deviation28822.41975
Coefficient of variation (CV)0.573469454
Kurtosis-1.196173129
Mean50259.7297
Median Absolute Deviation (MAD)24922
Skewness-0.01114460025
Sum2512986485
Variance830731880.1
MonotocityNot monotonic
2020-11-03T14:48:42.661284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
74011< 0.1%
 
74331< 0.1%
 
53841< 0.1%
 
279111< 0.1%
 
954921< 0.1%
 
852511< 0.1%
 
176661< 0.1%
 
59221< 0.1%
 
872961< 0.1%
 
274791< 0.1%
 
463321< 0.1%
 
340421< 0.1%
 
401851< 0.1%
 
223421< 0.1%
 
145331< 0.1%
 
873441< 0.1%
 
504181< 0.1%
 
83901< 0.1%
 
94541< 0.1%
 
811331< 0.1%
 
790841< 0.1%
 
33071< 0.1%
 
12901< 0.1%
 
688751< 0.1%
 
Other values (49975)49975> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
81< 0.1%
 
161< 0.1%
 
171< 0.1%
 
181< 0.1%
 
ValueCountFrequency (%) 
999991< 0.1%
 
999951< 0.1%
 
999901< 0.1%
 
999871< 0.1%
 
999851< 0.1%
 
999831< 0.1%
 
999811< 0.1%
 
999801< 0.1%
 
999791< 0.1%
 
999781< 0.1%
 

engine_capacity
Real number (ℝ≥0)

MISSING

Distinct80
Distinct (%)0.4%
Missing30050
Missing (%)60.1%
Infinite0
Infinite (%)0.0%
Mean1.867213033
Minimum0
Maximum9.5
Zeros34
Zeros (%)0.1%
Memory size390.8 KiB
2020-11-03T14:48:42.942495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q11.4
median1.8
Q32
95-th percentile3
Maximum9.5
Range9.5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.808439743
Coefficient of variation (CV)0.432965992
Kurtosis29.4342974
Mean1.867213033
Median Absolute Deviation (MAD)0.2
Skewness4.305145264
Sum37250.9
Variance0.653574818
MonotocityNot monotonic
2020-11-03T14:48:43.270723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
241138.2%
 
1.629976.0%
 
1.424855.0%
 
1.218913.8%
 
1.918223.6%
 
1.814993.0%
 
37831.6%
 
17081.4%
 
2.25791.2%
 
2.55241.0%
 
1.34050.8%
 
2.42250.4%
 
1.12180.4%
 
1.52030.4%
 
1.72000.4%
 
2.71800.4%
 
2.81640.3%
 
3.21450.3%
 
2.31110.2%
 
4.2860.2%
 
2.6560.1%
 
4440.1%
 
0340.1%
 
5320.1%
 
5.2260.1%
 
Other values (55)4200.8%
 
(Missing)3005060.1%
 
ValueCountFrequency (%) 
0340.1%
 
0.18< 0.1%
 
0.216< 0.1%
 
0.31< 0.1%
 
0.42< 0.1%
 
0.71< 0.1%
 
0.814< 0.1%
 
0.915< 0.1%
 
17081.4%
 
1.12180.4%
 
ValueCountFrequency (%) 
9.51< 0.1%
 
9.41< 0.1%
 
9.34< 0.1%
 
9.2250.1%
 
9.17< 0.1%
 
912< 0.1%
 
8.91< 0.1%
 
8.61< 0.1%
 
8.54< 0.1%
 
8.31< 0.1%
 

type
Real number (ℝ)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.04
Minimum-1
Maximum6
Zeros4329
Zeros (%)8.7%
Memory size49.0 KiB
2020-11-03T14:48:43.536329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median3
Q35
95-th percentile6
Maximum6
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.370745117
Coefficient of variation (CV)0.7798503674
Kurtosis-1.118899507
Mean3.04
Median Absolute Deviation (MAD)2
Skewness-0.3644499003
Sum152000
Variance5.620432409
MonotocityNot monotonic
2020-11-03T14:48:43.801936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
31339226.8%
 
51027020.5%
 
6942318.8%
 
-1625412.5%
 
043298.7%
 
132846.6%
 
226435.3%
 
44050.8%
 
ValueCountFrequency (%) 
-1625412.5%
 
043298.7%
 
132846.6%
 
226435.3%
 
31339226.8%
 
44050.8%
 
51027020.5%
 
6942318.8%
 
ValueCountFrequency (%) 
6942318.8%
 
51027020.5%
 
44050.8%
 
31339226.8%
 
226435.3%
 
132846.6%
 
043298.7%
 
-1625412.5%
 

registration_year
Real number (ℝ≥0)

Distinct120
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1839.1952
Minimum0
Maximum2016
Zeros229
Zeros (%)0.5%
Memory size390.8 KiB
2020-11-03T14:48:44.348773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q11998
median2003
Q32008
95-th percentile2016
Maximum2016
Range2016
Interquartile range (IQR)10

Descriptive statistics

Standard deviation545.9742433
Coefficient of variation (CV)0.296854974
Kurtosis7.106242715
Mean1839.1952
Median Absolute Deviation (MAD)5
Skewness-3.016393557
Sum91959760
Variance298087.8743
MonotocityNot monotonic
2020-11-03T14:48:44.676876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
200529105.8%
 
201627905.6%
 
200627315.5%
 
200026705.3%
 
199926545.3%
 
200326335.3%
 
200426175.2%
 
200126085.2%
 
200225295.1%
 
200723654.7%
 
200822004.4%
 
200920944.2%
 
199819904.0%
 
201017073.4%
 
201116513.3%
 
199715393.1%
 
201213072.6%
 
199611212.2%
 
20138471.7%
 
19958141.6%
 
20146501.3%
 
19944811.0%
 
20153880.8%
 
19923560.7%
 
19933270.7%
 
Other values (95)602112.0%
 
ValueCountFrequency (%) 
02290.5%
 
12220.4%
 
22060.4%
 
32530.5%
 
42400.5%
 
52540.5%
 
62240.4%
 
72280.5%
 
81970.4%
 
91940.4%
 
ValueCountFrequency (%) 
201627905.6%
 
20153880.8%
 
20146501.3%
 
20138471.7%
 
201213072.6%
 
201116513.3%
 
201017073.4%
 
200920944.2%
 
200822004.4%
 
200723654.7%
 

gearbox
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
1
37008 
0
10951 
-1
 
2041
ValueCountFrequency (%) 
13700874.0%
 
01095121.9%
 
-120414.1%
 
2020-11-03T14:48:44.989354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-03T14:48:45.239694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:45.505285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.04082
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
13904975.0%
 
01095121.0%
 
-20413.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5000096.1%
 
Dash Punctuation20413.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
13904978.1%
 
01095121.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2041100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common52041100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
13904975.0%
 
01095121.0%
 
-20413.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII52041100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
13904975.0%
 
01095121.0%
 
-20413.9%
 

power
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct452
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.10506
Minimum0
Maximum16311
Zeros4294
Zeros (%)8.6%
Memory size390.8 KiB
2020-11-03T14:48:45.838823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175
median110
Q3150
95-th percentile233
Maximum16311
Range16311
Interquartile range (IQR)75

Descriptive statistics

Standard deviation188.7879379
Coefficient of variation (CV)1.558877374
Kurtosis3513.072746
Mean121.10506
Median Absolute Deviation (MAD)37
Skewness52.35179232
Sum6055253
Variance35640.88548
MonotocityNot monotonic
2020-11-03T14:48:46.229416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
042948.6%
 
7531626.3%
 
15021964.4%
 
14019663.9%
 
6019533.9%
 
10119073.8%
 
11617213.4%
 
9016673.3%
 
17015983.2%
 
10515143.0%
 
1369731.9%
 
1259491.9%
 
1639091.8%
 
1028901.8%
 
1438421.7%
 
1227981.6%
 
1317681.5%
 
547071.4%
 
1106731.3%
 
1096561.3%
 
1205901.2%
 
805631.1%
 
505581.1%
 
1775471.1%
 
585371.1%
 
Other values (427)1706234.1%
 
ValueCountFrequency (%) 
042948.6%
 
11< 0.1%
 
23< 0.1%
 
43< 0.1%
 
514< 0.1%
 
65< 0.1%
 
73< 0.1%
 
82< 0.1%
 
92< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
163111< 0.1%
 
136361< 0.1%
 
136161< 0.1%
 
125121< 0.1%
 
120121< 0.1%
 
115301< 0.1%
 
110111< 0.1%
 
85001< 0.1%
 
75111< 0.1%
 
62261< 0.1%
 

model
Real number (ℝ)

Distinct248
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.90192
Minimum-1
Maximum246
Zeros61
Zeros (%)0.1%
Memory size97.8 KiB
2020-11-03T14:48:46.588783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4
Q131
median84
Q3154
95-th percentile224
Maximum246
Range247
Interquartile range (IQR)123

Descriptive statistics

Standard deviation72.46360852
Coefficient of variation (CV)0.7635631452
Kurtosis-0.9795563456
Mean94.90192
Median Absolute Deviation (MAD)55
Skewness0.4563148456
Sum4745096
Variance5250.97456
MonotocityNot monotonic
2020-11-03T14:48:46.950992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11741218.2%
 
4035417.1%
 
1129275.9%
 
-122574.5%
 
17216273.3%
 
8414863.0%
 
2914512.9%
 
16914312.9%
 
4314062.8%
 
6012352.5%
 
1511922.4%
 
9710992.2%
 
289852.0%
 
2218361.7%
 
1048351.7%
 
318341.7%
 
86981.4%
 
1036811.4%
 
2246111.2%
 
1075851.2%
 
335811.2%
 
65751.1%
 
2315081.0%
 
2194861.0%
 
2464831.0%
 
Other values (223)1752935.1%
 
ValueCountFrequency (%) 
-122574.5%
 
0610.1%
 
15< 0.1%
 
2720.1%
 
3810.2%
 
4310.1%
 
51600.3%
 
65751.1%
 
74< 0.1%
 
86981.4%
 
ValueCountFrequency (%) 
2464831.0%
 
2451210.2%
 
244330.1%
 
24321< 0.1%
 
2421470.3%
 
241560.1%
 
240270.1%
 
239260.1%
 
2383320.7%
 
237350.1%
 

mileage
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125206.2
Minimum5000
Maximum150000
Zeros0
Zeros (%)0.0%
Memory size390.8 KiB
2020-11-03T14:48:47.232219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile40000
Q1100000
median150000
Q3150000
95-th percentile150000
Maximum150000
Range145000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation39587.83684
Coefficient of variation (CV)0.3161811223
Kurtosis0.9775977899
Mean125206.2
Median Absolute Deviation (MAD)0
Skewness-1.474903011
Sum6260310000
Variance1567196825
MonotocityNot monotonic
2020-11-03T14:48:47.497830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
1500003178463.6%
 
125000532310.6%
 
10000021914.4%
 
9000018203.6%
 
8000016253.2%
 
7000013672.7%
 
6000013082.6%
 
5000011092.2%
 
400009762.0%
 
200008071.6%
 
300008001.6%
 
50006531.3%
 
100002370.5%
 
ValueCountFrequency (%) 
50006531.3%
 
100002370.5%
 
200008071.6%
 
300008001.6%
 
400009762.0%
 
5000011092.2%
 
6000013082.6%
 
7000013672.7%
 
8000016253.2%
 
9000018203.6%
 
ValueCountFrequency (%) 
1500003178463.6%
 
125000532310.6%
 
10000021914.4%
 
9000018203.6%
 
8000016253.2%
 
7000013672.7%
 
6000013082.6%
 
5000011092.2%
 
400009762.0%
 
300008001.6%
 

fuel
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.48458
Minimum-1
Maximum4
Zeros87
Zeros (%)0.2%
Memory size49.0 KiB
2020-11-03T14:48:47.779075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median2
Q32
95-th percentile2
Maximum4
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8465793274
Coefficient of variation (CV)0.5702483715
Kurtosis2.63990937
Mean1.48458
Median Absolute Deviation (MAD)0
Skewness-1.645279202
Sum74229
Variance0.7166965575
MonotocityNot monotonic
2020-11-03T14:48:48.044664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
22986859.7%
 
11566531.3%
 
-135837.2%
 
37771.6%
 
0870.2%
 
420< 0.1%
 
ValueCountFrequency (%) 
-135837.2%
 
0870.2%
 
11566531.3%
 
22986859.7%
 
37771.6%
 
420< 0.1%
 
ValueCountFrequency (%) 
420< 0.1%
 
37771.6%
 
22986859.7%
 
11566531.3%
 
0870.2%
 
-135837.2%
 

brand
Real number (ℝ≥0)

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.53908
Minimum0
Maximum39
Zeros314
Zeros (%)0.6%
Memory size49.0 KiB
2020-11-03T14:48:48.341718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median23
Q333
95-th percentile38
Maximum39
Range39
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.45816983
Coefficient of variation (CV)0.6552469649
Kurtosis-1.342794143
Mean20.53908
Median Absolute Deviation (MAD)13
Skewness-0.1378992627
Sum1026954
Variance181.1223352
MonotocityNot monotonic
2020-11-03T14:48:48.685427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
381081021.6%
 
2569611.4%
 
24508710.2%
 
20497710.0%
 
146409.3%
 
1031846.4%
 
2722414.5%
 
2515173.0%
 
911862.4%
 
309631.9%
 
318031.6%
 
197441.5%
 
327121.4%
 
56961.4%
 
236891.4%
 
366261.3%
 
125391.1%
 
215291.1%
 
334821.0%
 
394340.9%
 
223780.8%
 
153620.7%
 
113600.7%
 
03140.6%
 
263040.6%
 
Other values (15)17273.5%
 
ValueCountFrequency (%) 
03140.6%
 
146409.3%
 
2569611.4%
 
32580.5%
 
41750.4%
 
56961.4%
 
61380.3%
 
7540.1%
 
8990.2%
 
911862.4%
 
ValueCountFrequency (%) 
394340.9%
 
381081021.6%
 
37540.1%
 
366261.3%
 
352720.5%
 
341020.2%
 
334821.0%
 
327121.4%
 
318031.6%
 
309631.9%
 

damage
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing8266
Missing (%)16.5%
Memory size390.8 KiB
0
37721 
1
4013 
(Missing)
8266 
ValueCountFrequency (%) 
03772175.4%
 
140138.0%
 
(Missing)826616.5%
 
2020-11-03T14:48:49.077236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

zipcode
Real number (ℝ≥0)

Distinct7002
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51436.40392
Minimum1067
Maximum99998
Zeros0
Zeros (%)0.0%
Memory size390.8 KiB
2020-11-03T14:48:49.374092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1067
5-th percentile10117
Q130989
median50374
Q372415
95-th percentile93138
Maximum99998
Range98931
Interquartile range (IQR)41426

Descriptive statistics

Standard deviation25808.98566
Coefficient of variation (CV)0.5017649698
Kurtosis-0.9866818816
Mean51436.40392
Median Absolute Deviation (MAD)20734
Skewness0.03418460102
Sum2571820196
Variance666103740.7
MonotocityNot monotonic
2020-11-03T14:48:49.983442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
101151210.2%
 
65428740.1%
 
40764500.1%
 
44145490.1%
 
60311490.1%
 
52525470.1%
 
66333470.1%
 
32257460.1%
 
13357450.1%
 
65719450.1%
 
60386440.1%
 
53757430.1%
 
50354430.1%
 
61169410.1%
 
76437410.1%
 
77933410.1%
 
65549410.1%
 
31275400.1%
 
90763400.1%
 
41334400.1%
 
51065390.1%
 
65929390.1%
 
47877390.1%
 
56564380.1%
 
65232370.1%
 
Other values (6977)4882197.6%
 
ValueCountFrequency (%) 
106716< 0.1%
 
10697< 0.1%
 
10974< 0.1%
 
10997< 0.1%
 
11081< 0.1%
 
110910< 0.1%
 
11273< 0.1%
 
11297< 0.1%
 
113910< 0.1%
 
11569< 0.1%
 
ValueCountFrequency (%) 
999983< 0.1%
 
999961< 0.1%
 
999881< 0.1%
 
999861< 0.1%
 
999763< 0.1%
 
9997413< 0.1%
 
999553< 0.1%
 
9994714< 0.1%
 
998974< 0.1%
 
998944< 0.1%
 

insurance_price
Real number (ℝ≥0)

MISSING

Distinct501
Distinct (%)1.2%
Missing7331
Missing (%)14.7%
Infinite0
Infinite (%)0.0%
Mean421.3452389
Minimum10
Maximum38960
Zeros0
Zeros (%)0.0%
Memory size390.8 KiB
2020-11-03T14:48:50.296076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile50
Q1100
median230
Q3510
95-th percentile1370
Maximum38960
Range38950
Interquartile range (IQR)410

Descriptive statistics

Standard deviation679.4443592
Coefficient of variation (CV)1.612559717
Kurtosis637.4215876
Mean421.3452389
Median Absolute Deviation (MAD)150
Skewness16.00350852
Sum17978380
Variance461644.6373
MonotocityNot monotonic
2020-11-03T14:48:50.624179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7019633.9%
 
6018383.7%
 
8017003.4%
 
9014252.9%
 
5013722.7%
 
10012692.5%
 
11010582.1%
 
14010072.0%
 
1209922.0%
 
1309762.0%
 
1509251.8%
 
1608421.7%
 
1708331.7%
 
1807461.5%
 
407391.5%
 
1907121.4%
 
2106771.4%
 
2006581.3%
 
2306381.3%
 
2405981.2%
 
2205931.2%
 
2505861.2%
 
2605061.0%
 
2705011.0%
 
2904781.0%
 
Other values (476)1903738.1%
 
(Missing)733114.7%
 
ValueCountFrequency (%) 
101210.2%
 
201920.4%
 
303240.6%
 
407391.5%
 
5013722.7%
 
6018383.7%
 
7019633.9%
 
8017003.4%
 
9014252.9%
 
10012692.5%
 
ValueCountFrequency (%) 
389601< 0.1%
 
376201< 0.1%
 
295201< 0.1%
 
224701< 0.1%
 
220401< 0.1%
 
165901< 0.1%
 
164101< 0.1%
 
160601< 0.1%
 
132501< 0.1%
 
130801< 0.1%
 

price
Real number (ℝ≥0)

Distinct2331
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5746.90438
Minimum455
Maximum163800
Zeros0
Zeros (%)0.0%
Memory size390.8 KiB
2020-11-03T14:48:50.936675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum455
5-th percentile591
Q11365
median3185
Q37270
95-th percentile18655
Maximum163800
Range163345
Interquartile range (IQR)5905

Descriptive statistics

Standard deviation7688.683102
Coefficient of variation (CV)1.337882553
Kurtosis52.77745738
Mean5746.90438
Median Absolute Deviation (MAD)2184
Skewness5.134366152
Sum287345219
Variance59115847.84
MonotocityNot monotonic
2020-11-03T14:48:51.249255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13658141.6%
 
4558041.6%
 
10927181.4%
 
9106991.4%
 
22756601.3%
 
7285781.2%
 
5465541.1%
 
31855471.1%
 
9095131.0%
 
18204761.0%
 
7734740.9%
 
6824700.9%
 
6374610.9%
 
8644410.9%
 
8194400.9%
 
5914290.9%
 
40954250.9%
 
16384220.8%
 
14564160.8%
 
20024160.8%
 
10014120.8%
 
27304020.8%
 
11833950.8%
 
5003840.8%
 
11373810.8%
 
Other values (2306)3726974.5%
 
ValueCountFrequency (%) 
4558041.6%
 
4641< 0.1%
 
4671< 0.1%
 
4737< 0.1%
 
4774< 0.1%
 
4829< 0.1%
 
4831< 0.1%
 
4861< 0.1%
 
4913< 0.1%
 
49913< 0.1%
 
ValueCountFrequency (%) 
1638001< 0.1%
 
1592501< 0.1%
 
1583401< 0.1%
 
1542061< 0.1%
 
1410501< 0.1%
 
1383101< 0.1%
 
1365001< 0.1%
 
1355901< 0.1%
 
1254891< 0.1%
 
1233051< 0.1%
 

Interactions

2020-11-03T14:47:58.454803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:47:58.782895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:47:59.048505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:47:59.376604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:47:59.673478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:47:59.954690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:00.236115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:00.532949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:00.829820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:01.129471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:01.486909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:01.752513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:02.018124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:02.304432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:02.554396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:02.835274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:03.085279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:03.350862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:03.600848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:03.866472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:04.132241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:04.585361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:04.866568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:05.116550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:05.382303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:05.647888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:05.944742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:06.351096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:06.647951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:06.944803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:07.210555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:07.491805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:07.757397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:08.054246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:08.335594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:08.601218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:08.866824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:09.132553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:09.398164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:09.679393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:09.929376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:10.226380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:10.476362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:10.741951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:11.007557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:11.273287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:11.538894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:11.820146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:12.070108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:12.351469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:12.851434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:13.117040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:13.382744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:13.663975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:13.929600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:14.226556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:14.507784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:14.804660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:15.085870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:15.351631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:15.648488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:15.898450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:16.164171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:16.461028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:16.711009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:16.976615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:17.242339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:17.539197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:17.804802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:18.070429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:18.336127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:18.618626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:18.884237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:19.149838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:19.415443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:19.696697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:19.962300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:20.243682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:20.774896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:21.087373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:21.353114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:21.665591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:21.931199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:22.212543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:22.478128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:22.743735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:23.024967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:23.307199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:23.588453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:23.895109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:24.191983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:24.473196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:24.738821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:25.004425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:25.346299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:25.643154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:25.924386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:26.189990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:26.471220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:26.752452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:27.018079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:27.330761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:27.612011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:27.893242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:28.190231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:28.487066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:29.018288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:29.518399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:29.940259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:30.205997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:30.471620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:30.737210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:31.002821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:31.284173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:31.549780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:31.815979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:32.081586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:32.362794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:32.628403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:32.878403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:33.159812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:33.425442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:33.707627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:33.973231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:34.238837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:34.504464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:34.785676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:35.051283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:35.317078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:35.598330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:35.863936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:36.129522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:36.395269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:36.660874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:36.942086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:37.489051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:37.770282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:38.051513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:38.332901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:38.614128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:38.895346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:39.192213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:39.645287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:39.942143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-03T14:48:51.702351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-03T14:48:52.139821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-03T14:48:52.499301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-03T14:48:52.889917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-03T14:48:40.426811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:41.036132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:41.614338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-03T14:48:41.926835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Indengine_capacitytyperegistration_yeargearboxpowermodelmileagefuelbranddamagezipcodeinsurance_priceprice
0482982.002006014058150000250.049191380.04267
181047NaN-12016-10234150000-120NaN45896NaN2457
2927542.23201011751541250001100.059229930.010374
346007NaN-120000265401500002100.039365680.07098
476981NaN13110981500002250.055271NaN2365
59651NaN619990122601500002200.02819580.01415
643085NaN6199901653115000021NaN7373460.01091
794244NaN-12016-1552241250002270.086470NaN1228
8745681.6-120161105117150000238NaN7926830.02275
9208941.83200511161731500002230.023554330.04732

Last rows

Indengine_capacitytyperegistration_yeargearboxpowermodelmileagefuelbranddamagezipcodeinsurance_priceprice
4999030715NaN520041652421250002360.037269170.01820
4999179497NaN319941102991500002100.015518NaN500
4999222451NaN1200811508600002250.054317440.06643
4999358497NaN-1201618511150000220.066333NaN2002
4999423971.6-1201011059690000260.061184290.06961
49995504291.432006175117900002380.035745500.04686
49996644251.3541601031500002100.060386NaN864
4999790761NaN31996115015150000220.028309130.02275
4999839709NaN3200711226100000120.083623500.08144
4999925524NaN-1199610117150000-1380.026789220.01592